Research Organization of Information and Systems

The Institute of
Statistical Mathematics
2016 -2017

C O N T E N TS
■ Message from Director-General ——

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■ Institute Overview—

2■ Departments and Research Centers——

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■ ISM Projects ——NOE (Network Of Excellence) Project
Project for Fostering and Promoting
Statistical Thinking
Producing Data Scientists in the Big Data Era
Coop with Math Program

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■ Research Cooperation——
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■ Graduate School Program—
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■ Facilities and Equipment——
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■ Finance and Buildings——
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■ Organization——
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■ History—

Message from Director-General

The previous fiscal year was one in which the subject of artificial intelligence (AI) created media headlines seemingly every day. In particular,
the victory of the Google-developed AlphaGo AI system over an international Go master brought news of rapid progress in AI technology to thegeneral public in a way that was particularly easy to understand. Of course,
it was already some 20 years ago, in 1997, that an international chess grandmaster was defeated by the Deep Blue system developed by IBM. The dramatic transformation of the AI environment over the ensuing years can be
attributed above all to one single factor: the explosive growth of data. For
example, whereas the quantity of genomic sequence data that can be readin one second has increased roughly a millionfold over this interval, thespeed of computation — as characterized by Moore’s law, the empiricalformula describing the evolution of computer performance — has grown“only” by a factor on the order of ten thousand. This data explosion is thetrue breakthrough enabling modern AI technology.
The impact of AI is evident across a vast range of the modern world —from our daily lives in the world around us, to an industrial restructuring that might be termed a fourth industrial
revolution, as well as to the front lines of research and development in science and technology — and strategies
for making effective, timely, and appropriate use of rapidly growing quantities of data will be the keys to successin the global society of the future. The subject of machine learning, which is responsible for the core functionality of AI systems, shares many aspects of its conceptual structure and theoretical foundations with statisticalmathematics. For this reason, four years ago the Institute of Statistical Mathematics (ISM) created the ResearchCenter for Statistical Machine Learning, which quickly began making contributions to relevant fields. This
Center is one of five centers promoting the NOE (Network Of Excellence) Project. Moreover, because Japan faces
an overwhelming shortage of domestic data-handling professionals, the School of Statistical Thinking was established around the same time as the Center; the establishment of this School demonstrates our focus on training
new generations of data scientists by promoting the Project for Fostering and Promoting Statistical Thinking.
During the period covered by the Second Midterm Goals and Plans, which ended in March 2016, we areproud to say that, supported by the dual pillars of our NOE Project and the Project for Fostering and Promoting
Statistical Thinking, we accomplished our Second Midterm Goals and more: designing a research network,
training young personnel via OJT, advancing the Project for Fostering and Promoting Statistical Thinking, andproviding resources for updated and new supercomputers and High Performance Computing Infrastructure(HPCI), among other accomplishments. Moreover, the previous fiscal year was one that witnessed a further
entrenchment of our industry/government/academia collaborations. We completed an agreement with Tachikawa
City concerning collaboration and cooperation, and participated in the Tachikawa Residents Survey; by assistingwith events sponsored by the city, we were able to contribute to the region and to enhance our collaborative
relationship with it. In April 2016, the ISM officially entered the period covered by the Third Midterm Goals andPlans. In addition to fostering research activities that encompass both pure and applied work while maintaininga keen awareness of connections to the real world, our objectives include sharing the fruits of our research withindustrial, governmental, and academic institutions, as well as local communities throughout Japan, and strengthening the functionality of institutions.
One sometimes comes across news stories rosily predicting that AI will soon solve all societal problems, but
the methodology and techniques of today’s AI will never lead to solutions for some difficult social problems, suchas predicting natural disasters and other massive risk incidents, supporting mental-health care, and offering real,
not just virtual, replacements for the knowledge and skill of seasoned professionals in complicated manufacturing
processes. We are dedicated wholeheartedly to building a laboratory — based on the foundation of “intelligence”
at which statistical mathematics excels, including prediction, discovery, optimization, and control — to meet theneeds of academia and society. We ask for a continuation of the support and understanding you have shown the
ISM, and, more than ever, for your valuable advice and guidance as we proceed with this endeavor.

Tomoyuki Higuchi
Director-General
The Institute of Statistical Mathematics1Institute Overview

Basic Research
Department of Statistical Modeling
The Department of Statistical Modeling works on the structural modeling of physical phenomenon related tonumerous factors, and it conducts research on model-based statistical inference methodologies. By means of themodeling of spatially and/or temporally varying phenomena, complex systems, and latent structures, the department
aims to contribute to the development of cross-field modeling intelligence.

■ Spatial and Time Series Modeling Group —————————————————————————————————————The Spatial and Time Series Modeling Group works on the development and evaluation of statistical models, which function effectively interms of predicting phenomena or scientific discoveries, through data analysis and modeling related to space-time-varying phenomena.

■ Complex System Modeling Group —————————————————————————————————————————The Complex System Modeling Group conducts studies in order to discover the structures of complex systems, such as
nonlinear systems and hierarchical networks, through statistical modeling.

■ Latent Structure Modeling Group ——————————————————————————————————————————The Latent Structure Modeling Group works on the modeling of variable factors as latent structures existing behindvarious dynamic phenomena in the real world. It also conducts research on methodologies for inference computation
associated with structures on the basis of data related to phenomena.

Department of Data Science
The aim of the Department of Data Science is to contribute to the development of natural and social sciences byconducting research into the methodology of designing statistical data collection systems, measuring and analyzing
complex phenomena for evidence-based sciences, and performing exploratory multivariate data analyses.

■ Data Design Group ——————————————————————————————————————————————————The Data Design Group focuses on research toward designing statistical data collection systems and developing therelated data analysis methods in a variety of survey and experimental environments.

■ Metric Science Group —————————————————————————————————————————————————The Metric Science Group studies methods for measuring and analyzing complex phenomena to extract statisticalevidence behind them in the various fields of science.

■ Structure Exploration Group —————————————————————————————————————————————The Structure Exploration Group advances statistical and mathematical research by applying or developing exploratory
multivariate data analyses to clarify latent structures of real phenomena in various fields of both natural and social sciences.

Department of Mathematical Analysis and Statistical Inference
The Department of Mathematical Analysis and Statistical Inference carries out research into general statisticaltheory, statistical learning theory, optimization, and algorithms for statistical inference.

■ Mathematical Statistics Group ———————————————————————————————————————————The Mathematical Statistics Group is concerned with aspects of statistical inference theory, modeling of uncertain phenomena,
stochastic processes and their application to inference, probability and distribution theory, and the related mathematics.

■ Learning and Inference Group ————————————————————————————————————————————The Learning and Inference Group develops statistical methodologies to describe the stochastic structure of data
mathematically and clarify the potential and the limitations of the data theoretically.

■ Computational Inference Group ———————————————————————————————————————————The Computational Inference Group studies mathematical methodologies in the research fields of numerical analysis, optimization,
discrete mathematics, and control and systems theory for computation-based statistical inference, as well as their applications.

NOE-type Research
Risk Analysis Research Center
The Risk Analysis Research Center is pursuing a scientific approach to uncertainties and risks in society, which haveincreased with the growing globalization of society and economy. Our research projects are mainly seismology, finance,
resources, environmentology, medical-health science, database development and risk mathematics. The Center also
manages a network organization for risk analysis with the goal of contributing to creating a safe and resilient society.
2Research and Development Center for Data Assimilation
Research and Development Center for Data Assimilation aims to construct simulation models that can predict
the future and to design effective observation systems by means of “data assimilation”, which is a fundamental
technology integrating numerical simulations and observational data.

Survey Science Center
Founded on the accomplishments in social research by the Institute of Statistical Mathematics spanning over halfa century including the Study of the Japanese National Character and the cross-national comparative research on
national characteristics, the Survey Science Center was established in January of 2011 in order to facilitate further
growth of the aforementioned sets of research as well as the establishment of networking ties with both domestic
and international research organizations and the increase in the capacity to make contributions to wider society bycreating what we call the NOE (Network Of Excellence).

Research Center for Statistical Machine Learning
Machine learning is a research field associated with autonomous systems that can learn their behavior from data.
This field is based on both the statistical science concerning inference from data and computer science concerning
efficient algorithms. It can be applied to broad disciplines ranging engineering and information science to naturalscience, such as robotics and brain sciences. Our research center aims at supporting the academic community ofthis field, as well as producing influential research through various joint projects.

Service Science Research Center
The aim of the Service Science Research Center is to apply data-centric methodologies to the service fields — frommarketing, supply chain management, and management engineering, to the modeling of social systems. We integrate
the insights from these fields to establish data-centric service sciences as a common discipline, through collaborations
with universities and institutions worldwide, taking advantage of ISM’s Network Of Excellence (NOE) program.

Professional Development
School of Statistical Thinking
The mission of the School of Statistical Thinking is to plan and implement various programs for statistical thinking,from extension courses to a professional development program. The researchers affiliated with the school are often
involved with specific data analysis projects, which help them to gain hands-on knowledge of data science. We
expect such an experienced researcher will play an active role as a number-cruncher, as a modeler, or as a project
coordinator.

Research Support
Center for Engineering and Technical Support
The Center for Engineering and Technical Support assists academics and their collaborators in many ways:
managing computer systems and networks, editing and publishing journals, maintaining the library, and manage
extension courses.

■ Computing Facilities Unit ———————————————————————————————————————————————The Computing Facilities Unit is in charge of managing computer facilities, scientific software, networking infrastructure,and network security.

■ Information Resources Unit —————————————————————————————————————————————The Information Resources Unit is responsible for maintaining a library and an electronic repository, and is in charge ofplanning statistical education courses open to the public.

■ Media Development Unit ———————————————————————————————————————————————The Media Development Unit is in charge of publishing and editing of research results and PR brochures.

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Departments and Research Centers
Department of Statistical Modeling

Biosignal Processing and Biosystem Modeling Based on Statistical Analysis
N Visualization of neuronal and astrocytic activity,and investigation of the functional connectivity
among them
Spontaneous rhythmic synchronization of neuron andastrocyte are fundamental brain function for information transfer in the brain. Rhythmic respiratory activity
in the brain stem is one of the notable synchronization
phenomenon which is worth investigating the synchronization mechanism in the brain. The imaging techniqueprovides rich information on neural and astrocytic activities with large size data sets. However, there is a needfor the development of attentive and qualitative analysis
methods to distinguish neurons, inspiratory neurons
and astrocytes. In this study, we are aiming to develop
algorithms to extract the regions of excitatory neurons,
inspiratory neurons and astrocytes in the imaging dataand statistical method to quantify correlation and causality among them. So far we developed a visualization
strategy to distinguish inspiratory neurons and astrocyte
using statistical mapping methods (Fig.1).
(Joint research with Hyogo Medical College, Japan, Göttingen University, Germany and Kiel University, Germany)

N Development of a robust and simple brain function

examination method for iterative measurement
The brain signals during cognitive or occupationaltasks have to be iteratively measured to evaluate the recovering process of the cognitive function of the patientswith depression before and after medication and occupation therapy. There is a problem, however, that the effectof cure for brain cognitive function cannot always be appropriately diagnosed because of the suppression effectresulted from the acclimatization to repeated presentation of task. In this research, we are aiming to develop arobust and simple brain function examination method
against acclimatization to repeated stimulation and effective statistical analysis for detecting brain signals. We
have been recording cerebral blood flow using wearable
near infrared spectroscopy (NIRS) system during occupation therapy for the patients with depression and pursuing spatio-temporal analysis of the data.
(Joint research with Gunma University, Japan)

N Prediction and modeling of prognosis of acute

myelogenous leukemia
This project aims to construct an intercellular networkmodel of pathogenesis and its transition of acute myelogenous leukemia based on system theoretical approach,which enable us to interpret known prognostic factor result from inter cellar interaction and predict the effect ofchemotherapy and immunotherapy. Currently we are
studying a prediction method of the distribution of parameters, such as velocity coefficient, in the differential
equation model derived from the assumption that normal
T cells increase the number of leukemia cells as positive
feedback. For the sake of this purpose, we employ the
temporally changing data of the number of normal leukocytes and leukemia cells which was acquired from the patients receiving induction chemotherapy.
(Joint research with Kanazawa University, Japan)

Fumikazu Miwakeichi

Figure 1: Uniformized variance map of original imaging data (upper left panel ). Maximal correlation map and binary RFP image ( upper middle
paned ). The averaged waveform of the inspiratory burst and time points of the representative time frame (upper right panel ). Four
representative time frames of time-lagged correlation maps ( lower panels, thresholded at p=0.05 corrected by Bonferroni method ).

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Department of Data Science

Statistical Analysis of Ancient DNA and the History of Life Diversity
Inferring the molecular phylogenies using the maximum
likelihood method from DNA of extant species, you can statistically analyze the biological diversity among species.
You can also evaluate the biological diversity within species by comparing the orthologous DNA. However, it was
not possible to analyze the evolution of the past of biodiversity, including the extinct organisms defunct. In recent
years, how to technology and analyzes the data to extract
the DNA from such teeth and bones of extinct animals wasdeveloped. Analysis of ancient DNA is becoming possible
with the current. We statistically analyzed the evolution of
the diversity of life.

N Analysis of ancient DNA has been difficult
The DNA preservation state of the body is highly dependent on temperature, humidity and the hydrogen ion
concentration of the environment. Therefore, ancient DNA
research has been mainly deployed in bodies (Mammoth,Neanderthal, etc.) derived from the high latitudes of the
low temperature and dry environment. However, in orderto study the extinction of biota by humans, it is extremely
important to analyze the biota derived from the warm andFigure 1: A variety of species groups included in the DNA of fossil
samples. Most of the DNA sequences are derived fromthe contamination of the environment microbes.

Figure 2: Distribution of ratites. Extinct Aepyornis of Madagascar (Elephant bird ) and Moa of New Zealand.

humid regions. This environment is bad for the preservationof DNA. The amount of DNA is reduced, also fragmented.
Studies using this sample are extremely difficult. In addition, the Japanese archipelago is acidic deposition layer byactive volcanic activity, research is a very difficult situation.

N Prevent contamination and discriminant analysis
Analysis of ancient samples is also in the fight against
contamination. DNA from ancient organisms is in a poor
state of preservation. When you amplify the DNA, another
DNA of microbes attached to the sample would be amplified more than that. Further the other DNA of environmental microbes of the laboratory would be amplified morethan that too. You must have a technique used to minimize
the contamination is. Then, from a large number of the sequences read by the sequencer, to determine the original
DNA and another DNA of microbes, the development of statistical methods for more split were important. In this way,
we will be able to analyze the biological diversity from alarge base sequence derived from ancient organisms.

N Phylogenetic analysis of extinct Aepyornis
It was successful in large-scale sequencing at the genomic level of Aepyornis (Elephant bird) of Madagascar under
the tropical environment. A comparison of the DNA sequence of the ratites (flightless birds) and other birds, wewere able to estimate the branch age and stable phylogeny.
This ratites became clear that to maintain monophyly. Aepyornis was found to be most closely related to the kiwi
which is small ratites that live in New Zealand. Based on
this phylogeny with time axis, it was statistical analysis ofmorphological data of extant and fossil birds. As a result,
we were able to obtain new knowledge about the change ofbody size and the reproductive strategies that occurred in
the course of evolution.

Jun Adachi

Figure 3:
Phylogenetic tree of birds,
mainly the ratites. Aepyornis was the most closely
related to Kiwi.

5Department of Mathematical Analysis and Statistical Inference

Statistical Inference for Stochastic Processes and High-frequency
Financial Data Analysis
N High-frequency financial data analysis

N Specific problems of high-frequency financial data

Statistical analysis with stock price data is one of
the most significant studies to forecast and control
risks of financial assets. Daily stock price data wasusually used for this purpose. On the other hand,
there are many studies of statistical analysis of highfrequency financial data such as all intra-day transactions data, as it is getting more available. Such data
has second or millisecond time stamps, and therefore it has much more information compared to daily
data. High-frequency financial data is modeled bystochastic processes and we can specify risk measures of stocks by using statistical inference theory of
stochastic processes.

Though the study of statistical inference of stochastic processes has long history, we need recent
advanced theories like limit theorems of non-ergodic
stochastic processes, to construct models of highfrequency financial data. Moreover, the following two
problems of high-frequency data observations makestatistical analysis more difficult. The first one is thatsome empirical facts suggest the existence of additional noise when we model high-frequency data bystochastic processes. The second one is that we observe stock prices when transactions occur. So observation times must be different for different stocks.
This makes difficult to estimate covariance and correlation of stocks. To solve these two problems of observations, we need a new statistical method.
N Statistical analysis with stock limit order books

We study not only stock transactions data but also
limit order book data which is aggregation of sell andbuy limit orders of each investor. By analyzing these
data, we expect to be able to capture microstructureof stock market more precisely, and be able to modeltransaction costs. We model limit order books by
self-exciting point processes. Such point processes
are well studied in the study of earthquake. So collaboration between other departments is expected
to develop further studies of limit order books.
Teppei Ogihara
Figure 1: We need a new statistical method to deal with problems of noise and asynchronous observations.

Figure 2: An example of stock limit order books. ‘ask’ is the total amount of present sell orders foreach prices. ‘bid’ is the total amount of buy orders.

6Departments and Research Centers
Risk Analysis Research Center

Stochastic Modeling and Probability Forecasting of Seismicity
N Probability forecasting of seismicity
Underground faults and stress fields that act on earthquake faults can hardly be observed directly. Forecasting
earthquakes are challenging due to the complexity of theheterogeneity tectonic environments from place to place.
However, earthquake occurrence is not completely random,and probability forecast is still possible. The space-time
ETAS model, which forecasts future earthquake probability
based on past seismicity, has been recognized by worldwide researchers as a standard statistical model for seismicity. Especially for an earthquake prone country like Japan,
this model is important in earthquake hazard mitigation. We
are developing practicable forecasting system based on the
ETAS model for the purpose of earthquake hazard mitigation. For example, we are realizing the real-time probability
aftershock forecasts immediately after the main shock (Fig.1).

N Improving seismicity modeling
In order to improve the forecasting performance and todevelop better models for analyzing seismicity, we are working on the development of seismicity models in the following aspects:(a) Location dependent space-time model for wild and global seismicity. The triggering behaviors between earthquakes vary from place to place, due to different regional and physical characteristics of the earth crust. These
differences can be implied by the spatial variation of
the ETAS model parameters and can be evaluated by aBayesian method with smoothness priors are employedon the model parameters or by the maximum weighted
likelihood estimates (Fig.2).
(b) High resolution 3D modeling. Incorporating hypocenter
depths into seismicity modeling provides the potential
for forecasting seismicity with higher resolution andgives better seismic hazard estimation than those of 2Dmodeling.

Figure 1: Retrospectively forecasted seismicity before andafter the 2011-3-11 M9.0 Tohoku earthquake.

(c) Incorporating with more detailed earthquake information, including fault geometry and focal mechanisms.
For example, the earthquake rupture geometry is incorporated in the ETAS model. By this newly extended model together with the stochastic reconstruction method,
we can make inversion of the earthquake rupture geometry through seismicity (Fig.3).
(d) Incorporating with more relevant geophysical observations. With rapid development of observation technologies, more and more observational data are accumulated. The improvement moves beyond using only observed
seismicity, and attempts to find correlations between
seismicity and precursory non-seismic observations, including crustal deformation, underground water level,gravity, etc., with a potential outcome of improved earthquake forecasting models. For example, we have foundthat background seismicity rate of the ETAS model forthe swarm activities in the Izu volcanic region is systematically synchronized with the volumetric strain changes recorded at a nearby observatory.

Figure 2: Spatial variation of the p-values of the ETAS model ( left panel ) anddistribution map of the volcanic arcs in Japan ( right panel ).

Jiancang Zhuang

Figure 3: Earthquake rupture inverted based on
triggered seismicity. Red color represents that more earthquakes are directly triggered by the main shock.

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Research and Development Center for Data Assimilation

Towards Data-driven Materials Discovery
With state-of-the art technologies in data science,
we aim to discover novel functional materials. Target
materials include drugs, dyes, solvents, polymers,
polymeric composites, nanostructured materials.
With the comprehensive technologies of machinelearning, such as Bayesian modeling, kernel methods,
natural language processing, sparse learning, optimization theory, we create the fundamental methodology and research infrastructures of Materials Informatics.
N Materials Research by Information Integration

Initiative ( MI2 I )Materials Informatics is an emerging cross-disciplinary field aimed at combining materials scienceand data science. The Materials Genome Initiative(MGI), announced by President Obama in 2011, wasdesigned to create infrastructures that accelerate thepace of discovery and deployment of new materials
for innovative products twice as fast. The MGI white
paper has stated Materials Informatics as the key toachieving dramatic rationalization of time and costs
in developments. In Japan, the Japan Science andTechnology Agency (JST) launched the Materials Research by Information Integration Initiative (MI 2 I) at
National Institute for Materials Science (NIMS) in
2015. The Institute of Statistical Mathematics has
been designated to be a recommitment site of MI 2 I as
the central institute of data science in Japan.

N Role of data science in material discovery anddevelopment
The design space of materials developments is considerably high-dimensional. For instance, the chemical space of organic compounds consists of 10 60
potential candidates. The challenge is to discovernovel and promising materials from the huge spacethat exhibit desirable material properties. In the traditional procedure, computational chemistry methods, such as the first principles calculation, havebeen put at the central analytic tool. Scientists hypothesize material structures based on experienceand intuition, and properties of the designed materials are assessed computationally and experimentally. The data-centric approach has been paid much attention as a promising alternative that can promote
enormous savings on time and costs in the laborious
and time-consuming trial-and-error procedure.
N Bayesian approach to data-driven materials

discovery
One of our research goals is to create a novel material design method by the integration of machinelearning and quantum chemistry calculation. The
method begins by obtaining a set of machine learning models to forwardly predict properties of input
material structures for multiple design objectives.
These models are inverted to the backward modelthrough Bayes’ law. Then we have a posterior probability distribution which is conditioned by desired
properties. Exploring high probability regions of theposterior, it is expected to identify new materials possessing the desired target properties. Under industry-academia partnerships, we are putting into practice the Bayesian material design method.
Ryo Yoshida

Figure 1:
Snapshot of altered molecular structures through Bayesian molecular
design calculation.

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Departments and Research Centers
Survey Science Center

Research on Survey Methodology and Planning of Cooperative
Training Survey
N Process of social survey and research on

N Practice of survey research by ISM

survey methodology
The Institute of Statistical Mathematics (ISM) has
been conducting many social surveys for more thansixty years. One purpose of these surveys is to study
public opinions in Japan. Another important purpose is to study survey methodology.
Social surveys mainly consist of two processes,
as is depicted in Fig.1. One is the process of sampling(right hand side), and the other is the process ofmeasurement (left hand side), Each step in Fig.1 has
its own theme to be studied.

Formerly ISM’s surveys were administered by ourselves with the support from many universities, employing students as par-time interviewers. But thistradition has been lost since 1990’s, entrusting most of
the survey operation to survey company. Worsening
survey circumstances did not allow us to carry out
our survey with non-professional interviewers. This
also means that we have gradually lost opportunities
for research on survey methodology.

Figure 1: Process of social survey and subject of research on
survey methodology.

N Planning of collaborative training survey

More recently, social science departments of many
universities have undergone curriculum reformation
which places more emphasis on survey
practice. In line with this trend, Survey
Science Center (SSC) decided to administer what we call “Collaborative Training
Survey” under close cooperation with
university departments, in which we offer faculty members and students opportunities to participate in real-life surveys.
For example, we conducted a nationwide “SSP-I Survey” in 2010 with Osaka
University. In 2011 and 2014, ISM and National Institute for Japanese Language
and Linguistics carried out a language
survey in Tsuruoka City in Yamagata Prefecture, which has been a cooperative
project of the both institute since 1950. In
2015 visiting professor Toru Kikkawa of SSC and myself developed a new-type CAPI (Computer Assisted
Personal Interview; See Fig.2) system and utilized it
in a nationwide survey called “2015 Survey on Stratification and Social Psychology” (SSP2015 Survey).
Students and young researchers from several universities as well as members of SSC participated in thesesurveys.
N Future plan

Figure 2: CAPI system used in SSP2015 Survey. Two tablets forinterviewer and respondent were used. For the firsttime in Japan, nationwide survey was administered bythis mode.

We are planning to continue this survey practicefor the coming years, with the support of visiting professors from outside the institute. The goal of thisactivity is to broaden the basis of research on survey
methodology by fostering more survey professionals.
Tadahiko Maeda
9Research Center for Statistical Machine Learning

Urban Temperature Analysis Using Participatory Sensing Data “Tweets”— Toward Improvement of Urban Intelligence —Recently global warming amplifies the risk factors
for extreme weather event. The problem of urban heatwaves becomes serious. In order to deal with the problem, it is first important to accurately capture urban
temperatures. Although urban temperatures are periodically observed in several monitoring stations, thestations are sparsely distributed and the time interval
is coarse. In this study, we investigated a method ofinterpolating urban temperatures observed sparsely
by utilizing “heat-tweets” which are densely tweeted inurban areas and include words related to heat.
N Dependence between heat-tweets andtemperature observations
Here we studied dependence between heat-tweets
and temperature observations at the monitoring stations by using a copula model and confirmed the usability of heat-tweets for temperature interpolation. The
intensity of each heat-tweet is estimated by using ageneralized additive model and a logistic model withsome additional information such as distances frommain stations, park and river, daytime and nighttime
population densities and land-use categories. Table 1
lists lower and upper tail dependences between temperature/change in temperature and heat-tweet intensity. The upper tail corresponds to the case in which
the temperature is high, the change in temperature islarge and the heat-tweet intensity is high and the lower
tail corresponds to the opposite case. It is generally
Lower tail

Upper tailTemperature

0.40

0.00

Change in temperature

0.20

0.23

Table 1: Dependence between temperature/change in temperature and heat-tweets.

considered that the heat-tweet intensity is high when
the temperature is high. However, we found that in theupper tail where heat waves were serious, the change
in temperature is especially related to the heat-tweets.
N Interpolation of temperatures using S-BLUE
We used S-BLUE (spatial best linear unbiased estimation) framework to combine temperature data andheat-tweets and reconstruct an efficient spatial-temporal temperature field for intra-urban resolutions. The
framework involves Gaussian process and non-linear
influence on temperature data and heat-tweets is wellmodeled. Figure 2 shows the interpolated temperatures using S-BLUE. It is shown that densely distributed heat-tweets in the central area successfully capture
temperature-related information that could not be captured by monitored temperatures.
Research Center for Statistical Machine Learning
Urban Intelligence Research Project

Tomoko Matsui

Figure 2: Interpolated temperatures at 06:00, 12:00, and 18:00 on
August 25, 2012 using S-BLUE with ( right ) and without( left ) heat-tweets.

Figure 1: Tweets in August, 2012 in Tokyo ( left ) and JMA monitoring stations ( right ).

* This work is collaboratively conducted with Dr. Yoshiki Yamagata, Dr. Daisuke Murakami (NIES) and Dr. Gareth W. Peters (UCL).

10Departments and Research Centers
Service Science Research Center

Machine Failure Prediction Based on Big Data
Sensor networks are being introduced into the manufacturing industry, often referred to as Industrie 4.0 or
IoT (Internet of Things). Sensor-generated data are analyzed using the big data technologies and provide opportunities for new applications such as production
automation, yield optimization, and failure analysis.
The Service Science Research Center at ISM is collaborating with a manufacturing company and trying to apply statistical tools to machine failure prediction andspare part logistics optimization based on the prediction. Following the best practices of data analytics inindustries, we apply the framework of descriptive, predictive, and prescriptive data analytics.

N Descriptive data analytics
Descriptive data analytics is to explain what happened in the past from observed sensor data. Primary
tools for descriptive data analytics include visualization, clustering, and data mining. Figure 1 plots sensor
data (colored lines) over a time period and failure events(yellow stars for minor failures and red ones for severe
failures) for a specific machine unit. Similar plots canbe drawn for different units. Looking at these data, we
can build a hypothesis that certain sensor data may becapable of predicting future failure events.
N Predictive data analytics
Predictive data analytics is to build a mathematicalmodel that predicts future events based on the available data. Figure 2 shows machine learning results using
sensor data of a few days prior to a predicted failure.
The bar graphs represent the contributions of the sensors for this prediction.
N Prescriptive data analytics

Figure 1: Sensor data and failure event for a particular unit.

Figure 2: Prediction model based on machine learning.

Good prediction alone does not automatically produce good business results. Prediction must be translated into business decisions. Prescriptive data analytics is to help business decisions to maximize the utility(such as profit), often by applying optimization technologies. In our case, the prediction model can be used
for optimize spare parts allocation acrossmultiple depots so that necessary parts areavailable when a failure occurs. The utility
function is the sum of the benefit of cost reduction because of the parts availability
and the additional cost because of the misprediction. Figure 3 shows how prediction
accuracy affects the optimum stock of parts
based on this model.
Through the project, we learned that (1)data preparation takes a large amount oftime, and (2) the three phases of data analytics are not always staged, but more shortcycles of these three aspects of data analytics need to be repeated over the period of
the project. This project has been conducted as a Research Collaboration Start-up Program of the Project for Fostering and Promoting Statistical Thinking.

Hiroshi Maruyama
Figure 3: Optimization of part logistics.

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NOE ( Network Of Excellence ) Project
Building a Framework for Advancing Strategic Research andPursuing a New Approach to Collaborative Research
N Research and Educational Activities asa Biaxial Structure

The Institute of Statistical Mathematics (ISM) pursues
research and education along the two lines of basic research, and NOE (Network Of Excellence)-type research(we will explain about “NOE” later) and professional development. These are respectively conducted by the basic research departments along a horizontal axis, and theNOE-type research centers and the school for professional development along a vertical axis (Figure 1).
By its nature, the basic research departments (along
the horizontal axis) cut across and link various disciplines, developing tools for interdisciplinary research.
The field of statistical mathematics must itself evolve tomeet the changing needs of society and the data environment, and is therefore constantly evolving as a fieldof study. At the same time, there are approaches and directions that remain unchanged as the field evolves. For
that reason, we have chosen not to call it fundamental
research or foundational research but “basic research”,
to reflect both the fixed and evolving qualities of statistical mathematics. There are three basic research departments: Statistical Modeling, Data Science, and Mathematical Analysis and Statistical Inference. These departments
engage in cutting-edge research to develop methodologies for rational prediction and decision making, basedon data and existing knowledge. All tenure research staff
in ISM are assigned to one of these basic research departments.
On the other hand, the NOE-type research centers
and the school for professional development (along thevertical axis) are staffed by permanent researchers within ISM, project professors/researchers (post-doctoral
staff), and visiting professors and researchers.

Biaxial
Structure

Figure 1: Biaxial structure for research and education

12

School of Statistical Thinking

Service Science

Statistical Machine Learning

Survey Science

Mathematical
Analysis andStatistical Inference

Data Assimilation

Data Science

Development( Top-down )Risk Analysis(Bottom-up )Basic Research

Statistical
Modeling

NOE-type Research Professional

The current NOE-type research centers are “Risk
Analysis Research Center”, “Research and Development
Center for Data Assimilation”, “Survey Science Center”,“Research Center for Statistical Machine Learning”, and“Service Science Research Center”. These centers conduct research activities that interface statistical mathematics with individual scientific disciplines in order tofind solutions to urgent social problems.
We set up the School of Statistical Thinking as a schoolfor professional development, which also serves multiple programs of the project for fostering and promoting
statistical thinking. In the School of Statistical Thinking,researchers, students, and contract researchers fromprivate companies who have an eye on the creation of a
new statistical research field, was well as various otherpeople who realize the necessity of statistics in their particular research field, train together to foster statisticalthinking. Of special importance, young project researchers receive on-the-job training from their senior mentors
and thereby obtain the assorted skills of “statisticalthinking”.

N NOE ( Network Of Excellence) ProjectIn accordance with the second medium-term plan of
Research Organization of Information and Systems (ROIS),
ISM’s parent organization, ISM had set as a goal the establishment of NOEs (Networks Of Excellence) in statistical mathematics. With the formation of an NOE in each of
five research fields — “Risk Research”, “Next-Generation
Simulation”, “Survey Science”, “Statistical Machine Learning”, and “Service Science” — we have been conducting
joint research activities that serve to deepen collaborative relationships with other institutes, both within Japanand overseas, for these last six years. The aim of this proj-

Professor Emeritus, Tokyo Instituteof Technology

Dr. Takatoshi Imai

Adjunct Professor, Computational
Science Education Center, Kobe
University

Dr. Yoshio Oyanagi

President, The Japan
Pharmaceutical Manufacturers
Association

Mr. Masayo Tada

President, Toyota Technological
Institute at Chicago

Dr. Sadaoki Furui

Special Advisor, Japan Scienceand Technology Agency

Dr. Hiroyuki Yoshikawa

Director-General, Institute for
Monetary and Economic Studies,Bank of Japan

Mr. Kenichiro Watanabe

Table 1: Members of Advisory Board of “NOE Project”( as of April, 2016 )ISM Projects

ect is to establish new scientific methodologies (“Fourth
Paradigm”), develop hubs for cross-disciplinary exchanges, and create new styles of networked joint research.
The aforementioned five NOE research centers serve ascore hubs in each NOE domain.
To fulfill the goal of establishing the new scientificmethodologies (“Fourth Paradigm”) in a knowledge-basedsociety, in which the importance of knowledge goes beyond merely solving individual problems, NOE activities are being systematically pursued through the coordination and support efforts of the NOE Promotion Unit,
under the unified project guidelines decided by theManaging Committee of NOE Project. At the same time,
advice is also taken from the members of the Advisory
Board of NOE Project (see Table 1), made up of experts
from the industrial, academic, and government sectors.

N Future Conception of NOE Activities
The NOE Project, which is made possible by ISM’s
special focus in the cross-disciplinary field of “statisticalmathematics”, is attracting strong support from each ofthese communities. On the basis of this project, ISM, asan Inter-University Research Institute, will be providing
further opportunities to the industrial, academic, andgovernment communities for joint usage (of facilities) andjoint research. ISM decided to continue to promote thisNOE Project during the third medium-term plan of ROIS,
which has gone into effect as of April 2016.
As described above, the five NOE research centersserve as core hubs for their respective fields. ISM is promoting the signing of MOUs (Memorandums of -Understanding) with research organizations within Japan andoverseas, and the number of MOUs is increasing year
by year, including MOUs which transit multiple NOE research fields. The goal of ISM is general research in statistical mathematics, which is in demand by various research fields, both in the humanities and science. ISM
has to respond flexibly to requests from each community and contribute to them. ISM plans to reorganize its
research structure and projects. With its’ focus on methodology, ISM continues to plan research in each of thefive NOE research fields. Furthermore, with all research
activities concentrated under the umbrella of the NOE
Research Promotion Organization, the general body thatoversees the five NOEs, ISM is expanding and developing its NOE projects with the aim of establishing newscientific methodologies (“Fourth Paradigm”), creating
new research disciplines, and developing new styles ofjoint research. Figure 2 shows a diagram of the present
state of the NOE Project. For more up-to-date information, please visit the website.

http://www.ism.ac.jp/noe/en/

Risk Research NOE

Service Science
Research Center

Additional related
Organizations

The Institute of Statistical
Mathematics ( ISM )Managing Committeeof NOE Project

Related by MOA

Next-Generation
Simulation NOE

Risk Analysis
Research Center

Service Science NOE
Related Organizations
inside and outsideJapan

Organization Cooperation in ISM

Related Organizations
inside and outside
Japan

Risk Research
Network

Additional related
Organizations

Advice from Advisory Board

Advisory
Board of
NOE Project

Research andDevelopment Center
for Data Assimilation

Related Organizations
inside and outsideJapan

Additional related
Organizations

NOE Promotion Unit

Research Center
for Statistical
Machine Learning
Additional related
Organizations

NOE Research Promotion
Organization

Related Organizations
inside and outsideJapan

Statistical Machine Learning NOE

Survey Science
CenterAdditional related
Organizations

Related Organizations
inside and outsideJapan

Survey Science NOE

Figure 2: Relationship diagram of NOE ( Network Of Excellence )13

Project for Fostering and Promoting Statistical Thinking

Rapid development of information and communication technology has led to the explosion of data.
Now surrounded by “Big Data”, everybody is expected to “think statistically”. More than ever, there is aneed for data scientists who can handle such big data and are able to extract useful knowledge from it.
Meanwhile, Japanese higher education is exhibiting a deplorable lack of production capacity in terms ofdata scientists. This can be accounted for by the fact that no academic institution other than ISM has adepartment of statistics and the small number of statisticians in academia are isolated from each other,
being scattered over various disciplines. Hoping to gain a little traction on this problem, ISM establishedthe School of Statistical Thinking, into which we integrated all of our educational resources. The following
are the principal projects.

Research Collaboration Start-up
The Institute had already been providing a consultation service for statistical science, but alongwith the launch of the School of Statistical Thinking in November 2011, this service was reorganized
as a research collaboration start-up. This program,being one of the projects to foster and promotestatistical thinking, is mainly aimed at supporting
applied scientists and other non-experts. Expert
statisticians affiliated with the Institute give them
advice on statistical modeling, data analysis, andresearch. Some cases have developed into official
research collaborations, which are our primary
duty as an inter-university research institute. The
start-up program covers a variety of topics, ranging from introductory to more specialized studies.
Half of the clients are from the private sector, andthe rest are staff members of public organizations,

university professors, or students. The Institute accepts about 40 cases annually, some of which benefit society in diverse ways.

Open-type Professional Development ProgramThis is a spin-out program from ISM cooperative
research projects. Establishing a goal is an indispensable element of the proposal of a cooperative
research project. On the other hand, such goal
setting is irrelevant for a summer school program,study session, or retreat. Since the launch of the
School of Statistical Thinking, organizers of such

14group-oriented study programs can apply to theOpen-type Professional Development Program.
There are two categories under this program: oneis ‘workshop’ and the other is ‘intensive training foryoung researchers’. For FY 2016, five workshops
have been accepted after review.

ISM Projects

Statistical Mathematics Seminar Series
The Institute holds weekly seminar series on statistical mathematics every Wednesday. The seminars are led by in-house and external lecturers to
showcase their latest studies. These lectures arefree to attend. To view the seminar schedule andlearn more about the program, please visit the Institute of Statistical Mathematics website.
http://www.ism.ac.jp/index_e.html

Open Lecture
We hold an open lecture during Education andCulture Week every year (November 1 through7), to introduce the Institute’s activities and topromote statistical science. We invite lecturers
to speak on a timely topic relating to statisticalscience. The lecture is open to the general public.
For further information, please visit the website of
the Institute of Statistical Mathematics.
http://www.ism.ac.jp/kouenkai/

Tutorial Courses
The education program at ISM dates back to
1944, the year of founding. The Ministry of Education installed a training center within ISM to foster
technicians in numerical computation. After theWorld War II, this training center was relaunched
in 1947 to develop pollsters and census takers. It
helped to cultivate professionals in the field of statistical surveys, while a growing number of entries
from business and industry coming for various
types of training were also observed.
When ISM joined the Inter-University ResearchInstitute Corporation in 1985, the training center
was formally shut down. We nevertheless tried tomaintain 4 courses per year, relying on support
from the private sector. The number of courses
was tripled when ISM became an independentagency in 2005. Now the tutorial courses are operated by the School of Statistical Thinking, which
was established in 2011.
In the 2015 academic year, 14 courses were held
and the number of participants was 911. The total
number of courses held from 1969 to March, 2016

was 335, with a total of 23,957 participants. These
courses covered a wide range of fields from basic toapplied statistics. The schedule of tutorial courses
can be found on the website of the Institute of
Statistical Mathematics.
http://www.ism.ac.jp/lectures/kouza.html

15

Producing Data Scientists in the Big Data Era

ISM Projects

Innovations in Our Data Science Program and Business-Academia
Collaboration
N Data Analysis Hackathon
A hackathon is an event where computer programmers and others involved in software development collaborate intensively on software projects.
When the projects are data analysis tasks, it is often
called a ‘data science hackathon’ or a ‘data analysis
hackathon’. ISM hosted a data analysis hackathon
in February 2016. Each team consisted of three students, and six teams from five universities participated in this competition. Thanks to a certain entertainment company, about 10 million customer
purchase records were available, along with someindividual attributes of the customers. Each team
was allowed access to a virtual machine preparedin the ISM’s on-premise cloud system. Highly pro-

prietary data and essential programming tools were
all installed on each cloud instance. After a two-day
workout, all teams proposed their new businessplans to promote sales in a final presentation session.
N Data Science Research Plaza

Researchers funded by private-sector firms canmaintain a desk and phone in the School of Statistical Thinking. This program is subject to fees, andthe contract can be renewed annually. Supercomputer access is available to them as long as theysubmit their proposals to High Performance Computing Infrastructure; see the web site below. A
faculty mentor gives advice to the accepted funded researcher so that he or she can freely attend various events, such as seminars, workshops, conferences, and extension courses.
After learning the expertise of the ISM research staff, participants in this program
are invited to take advantage of paid consultants and funded research collaboration.
(http://www.hpci-office.jp/folders/english)

N Big Data Innovation Laboratory

Hackathon participants working on data analysis

Virtual machines prepared on the Communal Cloud Computing System

16

In August 2015, ISM together with SAS Institute Japan launched Big Data Innovation
Laboratory. This laboratory — actually, it isa service — provides a practice field for big
data handling with companies, governmental agencies, and public service organizations. Participants are allowed to use a virtual computer prepared in ISM’s on-premisecloud system, where SAS is already installed.
Using Hadoop from SAS lowers the hurdleof manipulating the distributed storage middleware for massive data. While they are
engaged in hands-on activities, they willdevelop skills for handling big data. Namely,
this is yet another program to increase thenumber of data scientists. After the trial use,
users faced with big data can judge whether
or not they should go ahead with investment
plans for an information system.

Coop with Math Program

ISM Projects

Towards the Formation of Research Foundations for Collaboration
between Mathematics, and Various Scientific Fields and Industries
N What is the Coop with Math Program?
The Coop with Math Program is a MEXT-funded
project which was originally planned in 2012 in an
atmosphere of promoting research via collaborations between mathematics and other scientificfields, as well as industries. The aim of this programis to provide mathematicians and researchers invarious scientific fields and industries with opportunities for inter-field discussions, and thus, to promote the finding of solutions to challenging problems via collaborations. The program was started
as a five-year project and commissioned to The Institute of Statistical Mathematics. The Institutecurrently manages the following activities along
main themes decided by the steering committee
under the Program with the aid of nine cooperating
institutions.

N Activities of the Coop with Math Program
The program mainly consists of supportingand organizing workshops, study groups, working
groups, and outreach activities for the generalpublic.
Workshops aim to discover new or challenging
problems which it is hoped will be solved by thecollaboration of various scientific fields and industries with mathematical science. Study groups,
which originally started at Oxford University in
1968, differ from workshops. Generally, coordinators first provide several problems and each participant chooses one of these and concentrates onsolving it for about a week. At the end of a study

group, participants who worked on the same problem together give presentations about solutions
or proposals for solutions to the problem. In fiscal
year 2015, the Program conducted 18 workshops
and 5 study groups focusing on various areas.
The Program independently supports finding
problems in specific fields which are expected tobe solved by mathematical science through organizing tutorial seminars and workshops, which include the activities of working groups. In fiscal year
2014, we produced a proposal for collaborations
between mathematical science and life science.
We also began to organize a community in which
all researchers can easily access activities not
only of mathematicians but also of material scientists, so that research via collaborations between
mathematics and materials science can be promoted. In fiscal year 2015, we have also supported
a tutorial seminar organized by National Instituteof Materials Science. Many people, among them
many researchers from various industries participated in this seminar.
The Program organizes various outreach activities not only for researchers but also for the general public. For example, we have organized talkexhibition events at Odaiba (2013, 2014, 2015),
which helped people feel more familiar with mathematical sciences. The Program also supports
career-path seminars so that students and academic and industrial researchers can exchange theirthoughts in detail.
By continuing these activities, we hopethat mutual comprehension and collaborations among researchers in mathematics
and those in other fields will be enhanced
as much as possible. Information about theProgram can be accessed through its website (http://coop-math.ism.ac.jp), which consists of the original application system, SNSssuch as Facebook (https://www.facebook.
com/CoopMath) and Twitter (@CoopMath),and our mail magazine. To learn more about
the Program, please visit these websites.

Panel discussion in Science Agora

17

Research Cooperation

International Cooperation■ Associated Foreign Research Institutes
Organization name

Address

The Statistical Research Division of the U.S. Bureau of the Census
Stichting Mathematisch Centrum
Institute for Statistics and Econometrics, Humboldt University of Berlin
Institute of Statistical Science, Academia Sinica
The Steklov Mathematical InstituteCentral South University
Soongsil University
Department of Statistics, University of Warwick
The Indian Statistical InstituteDepartment of Empirical Inference, Max Planck Institute for Biological Cybernetics
Faculudade de Medicina da Universidade de São Paulo
Department of Communication Systems,SINTEF Information and Communication Technology
Human Language Technology Department, Institute for Infocomm ResearchCentre for Computational Statistics and Machine Learning, University College London
Department of Electronics and Telecommunications, Norwegian University of Science and Technology
Department of Probability and Mathematical Statistics, Charles University in Prague
The Department of Ecoinformatics, Biometrics and Forest Growth of the Georg-August University of Goettingen
The Korean Statistical Society
Toyota Technological Institute at Chicago
Mathematical Sciences Institute Australian National University
RiskLab ETH Zurich
Institut de Recherche en Composants logiciel et materiel pour l’Information et la Communication Avancee ( IRCICA )USA ( Washington )Le laboratoire de mathematiques de I’Universite Blaise Pascal
Centre de Rechereche en Informatique, Signal et Automatique de Lille (CRIStAL) UMR CNRS 9189
University College London ( UCL ) Big Data Institute
The Institute of Forestry, Pokhara of Tribhuvan University
The Institute of Forest and Wildlife Research and Development of the Forestry Administration of Cambodia
The Chancellor masters and Scholars of the University of Oxford
Forest Inventory and Planning Institute of Vietnam
Zuse Institute Berlin
The University of PortoConclusion day

July 27, 1988
The Kingdom of the Netherlands ( Amsterdam ) May 10, 1989
Germany ( Berlin )December 8, 2004
Taiwan ( Taipei )June 30, 2005
Russia ( Moscow )August 9, 2005
China ( Changsha )November 18, 2005
The Republic of Korea ( Seoul )April 27, 2006
The United Kingdom ( Coventry )January 16, 2007
India (Kolkata )October 11, 2007
Germany ( Tubingen )August 11, 2010
Brasil ( Sao Paulo )April 15, 2011
Norway ( Trondheim )January 30, 2012
Singapore (Singapore )February 16, 2012
The United Kingdom ( London )February 16, 2012
Norway ( Trondheim )May 22, 2012
Czech Republic (Prague )October 10, 2012
Germany (Goettingen )October 18, 2012
The Republic of Korea ( Seoul )July 9, 2013
USA ( Chicago )February 10, 2014
Australia ( Canberra )May 15, 2014
Switzerland ( Zurich )February 7, 2015
France ( Paris )February 9, 2015
France (Clermont-Ferrand )February 11, 2015
France ( Paris )February 12, 2015
The United Kingdom ( London )February 26, 2015
Nepal ( Pokhara )March 6, 2015
Cambodia ( Phnom Penh )March 6, 2015
The United Kingdom ( Oxford )March 10, 2015
Vietnam (Hanoi )June 2, 2015
Germany (Berlin )June 20, 2016
Portugal ( Porto )June 22, 2016

Research Collaboration
This academic study program provides research­ers from other academic institutes with access to the facilitiesof the Institute, and provides opportunities for researchers to conduct theoretical and applied studies on statistics.

■ Number of Activities

2010
135

2011
172

2012
182

2013
181

2014
177

2015
183

■ Fields of Research Collaboration
Research collaboration is classified by research field as follows. Applicants can use the table below to findthe most appropriate type of project.
ISM Fields
Numberabc

FieldsNumberSpatial and Time Series
Modeling Group
Complex System Modeling Group
Latent Structure Modeling Group

d
e
f
g

FieldsNumberData Design Group
Metric Science Group
Structure Exploration Group
Mathematical Statistics GrouphijFieldsLearning and Inference Group
Computational Inference Group
OthersMajor Research Fields
NumberFieldsMajor Research Domains

4

Statisticalmathematics
Information
science
BiologicalsciencePhysicalscience5Engineering

Mathematical theory of statistics,
optimization, etc.
Algorithms, use of computerin statistics, etc.
Medicine, pharmacy, epidemiology,
genetics, etc.
Space, planet, earth, polar region,materials, etc.
Mechanics, electronics, control,chemistry, architecture, etc.

1
2
3

18

NumberFieldsMajor Research Domains

6
7

Human
science
Socialscience

Philosophy, art, psychology, education,
history, geography, culture, language, etc.
Economics, law, politics, society, manage­
ment, official statistics, population, etc.

8

Environmental
science

Environmental Statistics, Environmetrics,
Agricultural Statistics, Land Economics,
Landscape Management, Forest
Management, etc.

9

OthersOther research fields

Graduate School Program

Organization
The Institute of Statistical Mathematics is one of the platforms of SOKENDAI (the Graduate University for
Advanced Studies; the headquarters in Hayama, Kanagawa), which was opened in October 1988 to offer graduate education. Since its opening, the Institute has included the Department of Statistical Science and, sinceApril 1989, has accepted students for education and research in doctoral programs. In 2006, the Instituteadopted a five-year system, offering either a five-year education and research program, or a three-year education and research program starting from the third year of study.
Regional Studies
Comparative Studies

School of Cultural and Social Studies

Japanese Studies
Japanese History
Cyber Society and Culture
Japanese Literature
Structural Molecular Science
Functional Molecular Science

School of Physical Sciences

Astronomical Science
Fusion Science
Space and Astronautical Science

President

Accelerator Science

School of High Energy Accelerator Science

Materials Structure Science
Particle and Nuclear Physics
Statistical Science

School of Multidisciplinary Sciences

Polar Science
Informatics
Genetics

School of Life Science

Basic Biology
Physiological Sciences

School of Advanced Sciences

Evolutionary Studies of Biosystems

Outline of Education and Research
The course includes designing of data-gathering systems, modeling, inference and forecasting in order toextract information and knowledge from the real world based on empirical data, as well as basic, mathematicaland applied education and research related to these fields. The course aims to provide the student with skills
that help to contribute to solving important and connected issues and give the ability to perform original research.
Field of Education and ResearchContents

Statistical Modeling

Education and research focuses on dynamic modeling such as spatial and space-time modeling, graphicalmodeling of temporally and/or spatially inter-related complex phenomena, and intelligent information
processing. We also provide education and research on statistical inference based on various models,
methods of calculation for inference, and evaluation of models based on data.

Data Science

We provide education and research on data design, investigation and analytical methods to cope with theuncertainty and incompleteness of information, as well as on computational statistics.

Mathematical andStatistical Inference

We provide education and research on the theory of statistics and related basic mathematics, statistical learningtheory to extract information from data by automated learning and inference techniques; as well as theory andapplications of optimization and computation algorithms which serve as the basis for computational inference.

19

Features of Education and Research● The course is the only integrated doctoral program on statistical science in Japan. It has received students

from a wide variety of disciplines and has offered education and research on statistical science by professors specialized in many different fields, from theory through to practical applications.

● The Institute of Statistical Mathematics, the platform for the course, is equipped with a world-class supercomputer, high-speed 3D graphic computers and simulators to generate physical random numbers, as well
as a variety of software, including original statistical software developed by the Institute.

● The academic publications and books on statistical and mathematical sciences produced are some of thebest in the world.

● In its role as an inter-university research institute, the Institute holds frequent workshops and seminars byvisiting professors and researchers from both Japan and abroad. Students are free to attend and participate.

● It is possible to collaborate with researchers from other universities and institutions. It is also possible forstudents to develop their own projects by participating in research projects with other institutions throughthe Transdisciplinary Research Integration Center, Research Organization of Information and Systems.

Course Requirements and Type of Degree Granted● Requirements to complete the doctoral course are as follows:

Completion of at least 40 credits, including the required ones, by a student in the five-year program, or completion of at least 10 credits, including the required ones, by a three-year doctorate student who previously
completed a Master’s course; meeting all the criteria set by the thesis committee of the Institute; and successfully completing the final examination.

● On completion of the course, either a Doctorate in Statistical Science or, if the thesis deals mainly with aninter-disciplinary field related to statistical science, a Doctorate of Philosophy is awarded.

● The required number of years of study will be flexible if a student demonstrates outstanding research results.

Number of Students ( As of April 1, 2016 )■ Doctor’s course five years■ Doctor’s course three years

Year of enrollment

2010

2012

2013

2014

Year of enrollment

2011

2012

2013

2014

2015

2016

Number of students

1

2

2

2

Number of students

2

2

1

8  (1 )2

4

* The figures in parentheses indicate the number of foreign students being supported at government expense.
* The figures in circles indicate those who are employed by other organizations.

University Background of Students
National and public universities• Hokkaido University ( 3 ) • Tohoku University ( 3 ) • Fukushima University (1) • University of Tsukuba (6 ) • Saitama University (1)• Chiba University (1) • Ochanomizu University (1) • National Graduate Institute for Policy Studies (1) • Tokyo Medical and Dental
University (1) • Tokyo University of Marine Science and Technology (1) • Tokyo Gakugei University ( 2 ) • Tokyo Institute ofTechnology (4 ) • The University of Tokyo (10 ) • Tokyo Metropolitan University (1) • Tokyo University of Agriculture and Technology (1)• Hitotsubashi University (6 ) • Shizuoka University (1) • Japan Advanced Institute of Science and Technology (1) • Nagoya University (3 )• Toyohashi University of Technology ( 2 ) • Kyoto University (6 ) • Osaka City University (1) • Osaka University ( 2 ) • Nara Institute of
Science and Technology (1) • Okayama University ( 2 ) • Shimane University (3 ) • Kyushu University ( 2 ) • Oita University (1)20Graduate School Program

University Background of Students
Private universities• Kitasato University (1) • Keio University (8 ) • Shibaura Institute of Technology (1) • Chuo University (7 ) • Tokyo University of
Science (6 ) • Toyo University (1) • Japan Women’s University (1) • Nihon University (2 ) • Hosei University (7 ) • Waseda University (8 )• Nanzan University (1) • Kansai University (1) • Kyoto Sangyo University (1) • Ritsumeikan University (1) • Okayama University of
Science (1) • Kurume University (1)Foreign universities• Aston University (1) • University of California, Irvine (1) • University of Campinas (1) • University of Colorado at Boulder (2)• University of Dhaka (2) • University of Hawaii (1) • Jahangirnagar University (2) • University of Malaya (1) • Northeast Normal
University (1) • Ohio University (2) • University of Rajshahi (2 ) • Stanford University (1) • Chinese Academy of Sciences, Institute ofApplied Mathematics (1) • University of Science and Technology of China (1) • Center for Analysis and Prediction, China Seismological
Bureau (1) • Northeastern University of Technology, China (1) • The Hong Kong University of Science and Technology (1)Degrees Awarded
Year

2010

2011

2012

Doctor of Philosophy

7 [1]

4

6 [1]

2013

2014

2015

6

5

5

* [ ] Ph.D. on the basis of the dissertation only (included in the total)

AlumniNational and public universities, and public organizations• Obihiro University of Agriculture and Veterinary Medicine • University of Tsukuba • University of Hyogo • The University of Tokyo• The University of Electro-Communications • Saitama University • Nagoya University • Kyushu University • Kyushu Institute ofTechnology • University of the Ryukyus • The Institute of Statistical Mathematics • Tohoku University • Yokohama National University• Hokkaido University • Tokyo Institute of Technology • Hiroshima University • Oita University of Nursing and Health Sciences• JAXA’s Engineering Digital Innovation Center • Kyoto University • Nara Institute of Science and Technology • Bank of Japan• Japan Broadcasting Corporation • Railway Technical Research Institute • Statistical Information Institute for Consulting and Analysis• Government Pension Investment Fund • Public School
Private universities• Sapporo Gakuin University • Tokyo Health Care University • Meiji University • Doshisha University • Josai University • Nihon
University • Komazawa University • Aichi University of Technology • Tokyo University of Information Sciences • Shibaura Instituteof Technology • Rikkyo University • Waseda University
Foreign universities• Jahangirnagar University • Victoria University • Massey University • University of Otaga • Statistics New Zealand • University of
Rajshahi • University of California, Los Angeles • Asia-Pacific Center for Security Studies Department • Central South University• Hong Kong Baptist University • University of South Carolina • The University of Warwick
Private companies, etc.
• Hitachi, Ltd. Central Research Laboratory • NTT Communication Science Laboratories • Seiwa Kikaku • NLI Research Institute• Mizuho Trust and Banking • Nomura Securities Co., Ltd. • ATR Computational Neuroscience Laboratories • Toyota Motor
Corporation, Higashi-Fuji Technical Center • Schlumberger Limited • Macquarie Securities, Japan • Non-Life Insurance Rating
Organization of Japan • Barclays Global Investors • Open Technologies Corporation • Yamaha Corporation • Goldman Sachs
Asset Management L.P. • CLC bio Japan, Inc. • Bank of Tokyo-Mitsubishi UFJ • Pfizer Japan Inc. • Doctoral Institute for Evidence
Based Policy • Sony Corporation • NTTIT Corporation • Sompo Japan Insurance Inc. • Qualicaps Co.,Ltd. • Bridgestone
Corporation • Brain Pad Inc. • Sumitomo Chemical Co.,Ltd. • PricewaterhouseCoopers Aarata • Mitsubishi Tanabe Pharma
Corporation • Daiichi Sankyo Co.,Ltd. • Shizuoka Cancer Center • CPC Clinical Trial Hospital, Medipolis Medical Research Institute• CRD Association • Japan Society for the Promotion of Science21Facilities and Equipment

Computational Resources ( As of April 1, 2016 )Since July 2014, the ISM has maintained three different
supercomputer systems: the Supercomputer System for
Data Assimilation (nicknamed “A”), the Supercomputer
System for Statistical Science (nicknamed “I”), and theCommunal Cloud Computing System (nicknamed “C”).
Those are nicknamed in tribute to a former director general, the late Professor Hirotugu Akaike, who invented thefamous model selection criterion AIC. As of July 2015, thesystem I has just been expanded to have Haswell nodes.
System A is the world’s largest shared-memory supercomputer system and consists of two sets of SGI UV 2000
(each 256 nodes of 10-core Xeon E5-4650v2, 64 TB memory). Half of this system is taking part in the high-performance computing infrastructure (HPCI) project.
System I is a large distributed-memory supercomputer that consists of 400 nodes of SGI ICE X with Ivy Bridgeand 136 Haswell nodes, having 12,864 cores in total. The
system also includes a large-scale shared storage system(2.5 PB disk storage), physical random number generator boards, and a visualization system that supports a

The Supercomputer System for Data
Assimilation ( nicknamed A )maximum resolution of 4,096 × 2,160 and has a 200-inch
rear projection screen for 3D visualization.
System C consists of 64 Dell PowerEdge R620 (two10-core Xeon E5-2680v2, 256 GB memory). This systemprovides easy-to-use computing environments, such as
distributed-memory statistical computing environments
and Web servers, running on Apache CloudStack software.
In the main office, the primary local area network(LAN) consists of an Ethernet network using 10GBASESR for the main trunk and 1000Base-T for branches. The
personal computers in researchers' offices, and systems
A, I, and C are all connected to this network. A wireless
LAN system is also available in the immediate area of thebuilding occupied by the institute. These LAN systems
enable distributed processing and allow computing resources and statistical data to be used effectively. Comprehensive network security methods have been implemented, such as a firewall system, anti-virus software,and an intrusion prevention system. To encourage joint
research with researchers both in Japan and abroad, as
well as the exchange of e-mails, the network is connectedto the Internet through SINET5 (40 Gbps).

The Supercomputer System for Statistical
Science ( nicknamed I )The Communal Cloud Computing
System ( nicknamed C )Library and Materials ( As of April 1, 2016 )We have a large number of major Japanese/foreign
journals covering a wide variety of fields including statistics, mathematics, computer science and informatics. In
addition, we also have a large library consisting of books
on humanities, social science, biology, medical science,science and engineering.
Besides contributed to Japanese and foreign pub­lications, we also have a collection of journals that wepublish ourselves: Annals of the Institute of Sta­tistical
Mathematics (English; Springer), Proceedings of the Institute of Statistical Mathematics (Japanese), ISM Survey
Research Report (Statistical Researches mainly related
to the Japanese National Character), Computer Science
Monographs, Cooperative Research Re­ports (for collaborative research projects), Research Memorandum, ISM

22

Reports on Statistical Computing, and ISM Report on
Research and Education.
All materials are properly catalogued and can besearched from the web in order to meet the needs ofresearch­ers working in a wide of fields. We also accept
photocopy requests.
Japanesebooks
18,58827%

Books
67,891
Foreign books
49,303
73%

Japanese
periodicals
993
45%

Foreignperiodicals

Periodicals
2,223

1,230
55%

Finance and Buildings

Administration Subsidy and Others ( 2015 )Type

Personnel expenses

Non-personnel expenses

Total

702,384

861,096

1,563,480

Expenditure

Unit: ¥1,000

Accepted External Funds ( 2015)
Type

Joint research

Subcontracted research,
Trustee businessContract
researchers

Contribution for
scholarship

Total

Items

17

20

3

5

45

Income47,320160,431

836

5,150

213,737
Unit: ¥1,000

Grant-in-Aid for Scientific Research “ KAKENHI” ( 2015)
Research Category

Items

Amount Granted

Grant-in-Aid for Scientific Research on Innovation Areas

2

12,200Grant-in-Aid for Scientific Research ( S )——Grant-in-Aid for Scientific Research ( A)2

17,420Grant-in-Aid for Scientific Research ( B)

1039,650Grant-in-Aid for Scientific Research ( C)19

25,870Grant-in-Aid for Challenging Exploratory Research

5

8,580

Grant-in-Aid for Young Scientists ( B )

9

9,534

Grant-in-Aid for Research Activity Start-up

4

4,550

Grant-in-Aid for JSPS Fellows

1

780

52

118,604

Total

Unit: ¥1,000

Site and Buildings ( As of April 1, 2016 )Site Area

62,450m 2

Area for Buildings (total)

16,209m 2

23

Organization

Organization Diagram ( As of August 1, 2016 )■ Spatial and Time Series Modeling Group

Cooperative
Research
Committee

Department of Statistical Modeling■ Latent Structure Modeling Group■ Data Design Group

Department of Data Science■ Metric Science Group■ Structure Exploration GroupCouncil

Scientific
Advisory
Board■ Complex System Modeling Group

Department of Mathematical
Analysis and Statistical Inference■ Mathematical Statistics Group■ Learning and Inference Group■ Computational Inference Group

Risk Analysis Research Center
Research and Development
Center for Data Assimilation

DirectorGeneral

Survey Science Center
Research Center for
Statistical Machine Learning

Vice DirectorGeneral

Service Science ResearchCenterSchool of Statistical Thinking

Center for Engineering andTechnical Support■ Computing Facilities Unit
■ Information Resources Unit
■ Media Development Unit

Library■ URA Station

Planning Unit

■ Office of Female Researcher
Development, ISM

Evaluation Unit
Administration Planningand Coordination Section

Information and Public Relations UnitIntellectual Property Unit
NOE Promotion Unit

National Institute ofPolar Research

Planning Section ( NIPR )NIPR / ISM
Joint Administration Office

The Institute of
Statistical Mathematics

Planning Section ( ISM )General Service Center24

Number of Staff ( As of April 1, 2016 )DirectorGeneral

Type

Director-General

Assistant Administ­ Technical
Professor Associate
Staff
Professor Professor rative Staff

Total

1

1

Department of Statistical Modeling

5

8

1

14

Department of Data Science

5

7

3

15

Department of Mathematical Analysis and Statistical Inference

9

3

2

14

School of Statistical Thinking

0Center for Engineering and Technical Support

1010

Administration Planning and Coordination Section

1

NIPR/ISM Joint Administration Office

13 ( 31)

1 ( 2)

14 ( 33)

14 ( 31)11 ( 2 )

69 ( 33)

Total

1

19

18
(

6

1

) Total number of staff of NIPR/ISM Joint Administration Office.

The number under Technical Staff at the Center for Engineering and Technical Support and Administrative Staff at the NIPR/ISM Joint Administration Officeinclude one each staff member who retired because of age but was reemployed in a different position.

Staff ( As of August 1, 2016 )Director-General

Tomoyuki HIGUCHI

Vice Director-General

Yoshiyasu TAMURA

Vice Director-General

Satoshi ITO

Vice Director-General

Koji KANEFUJI

Department of Statistical Modeling
Director Junji NAKANO• Spatial and Time Series Modeling Group —————————————————————————————————————————————————————
Prof.

Nobuhisa KASHIWAGIProf.

Tomoyuki HIGUCHI

Assoc. Prof.

Genta UENO

Assoc. Prof.

Shinya NAKANOAssoc. Prof.

Jiancang ZHUANG• Complex System Modeling Group ——————————————————————————————————————————————————————————
Prof.

Yoshiyasu TAMURA

Prof.

Junji NAKANOProf.

Yukito IBA

Assoc. Prof.

Yumi TAKIZAWA

Assoc. Prof.

Fumikazu MIWAKEICHI

Assoc. Prof.

Shinsuke KOYAMA

Assist. Prof.

Ayaka SAKATA

Visiting Prof.

Michiko WATANABE

Visiting Prof.

Kenichi MIURA

Visiting Prof.

Hiroko NAKANISHI

Visiting Prof.

Hiroshi MARUYAMA

Visiting Prof.

Makoto SHIMIZU

Visiting Assoc. Prof. Toru ONODERA

Visiting Assoc. Prof. Fumitake SAKAORI

Visiting Assoc. Prof. Yuji MIZUKAMI• Latent Structure Modeling Group ——————————————————————————————————————————————————————————Prof.

Tomoko MATSUIProf.

Yoshinori KAWASAKI

Assoc. Prof.

Kazuhiro MINAMI

Project Researcher

Guo Zhongliang

Assoc. Prof.

Ryo YOSHIDA

Department of Data Science
Director Satoshi YAMASHITA• Data Design Group ———————————————————————————————————————————————————————————————————
Prof.

Takashi NAKAMURA

Prof.

Ryozo YOSHINOProf.

Takahiro TSUCHIYA

25

Department of Data Science
Assoc. Prof.

Naomasa MARUYAMA

Visiting Prof.

Takatoshi IMADA

Assoc. Prof.

Tadahiko MAEDA

Assist. Prof.

Masayo HIROSE• Metric Science Group —————————————————————————————————————————————————————————————————
Prof.

Satoshi YAMASHITA

Assoc. Prof.

Kenichiro SHIMATANI

Assoc. Prof.

Masayuki HENMI

Assoc. Prof.

Ikuko FUNATOGAWA

Assoc. Prof.

Hisashi NOMA

Assist. Prof.

Nobuo SHIMIZU

Project Researcher

Ai KAWAMORI• Structure Exploration Group —————————————————————————————————————————————————————————————
Prof.

Koji KANEFUJI

Assist. Prof.

Yoo Sung PARK

Manabu KUROKIProf.

Assoc. Prof.

Jun ADACHI

Department of Mathematical Analysis and Statistical Inference
Director Satoshi KURIKI• Mathematical Statistics Group ———————————————————————————————————————————————————————————————
Prof.

Satoshi KURIKI

Assoc. Prof.

Shuhei MANO

Assoc. Prof.

Shogo KATO

Assist. Prof.

Takaaki SHIMURA

Assist. Prof.

Teppei OGIHARA

Visiting Prof.

Akimichi TAKEMURA• Learning and Inference Group —————————————————————————————————————————————————————————————
Prof.

Shinto EGUCHIProf.

Kenji FUKUMIZU

Prof.

Hironori FUJISAWA

Prof.

Shiro IKEDA

Assoc. Prof.

Daichi MOCHIHASHI

Visiting Assoc. Prof. Syuichi KAWANO• Computational Inference Group ———————————————————————————————————————————————————————————
Prof.

Yoshihiko MIYASATO

Prof.

Akiko TAKEDA

Prof.

Atsushi YOSHIMOTO

Prof.

Satoshi ITO

Risk Analysis Research Center
Director Satoshi YAMASHITA
Prof.

Satoshi YAMASHITA

Prof.

Satoshi KURIKI

Prof.

Shinto EGUCHIProf.

Koji KANEFUJI

Prof.

Nobuhisa KASHIWAGI

Prof.

Atsushi YOSHIMOTO

Prof.

Yoshinori KAWASAKIProf.

Manabu KUROKI

Assoc. Prof.

Masayuki HENMI

Assoc. Prof.

Fumikazu MIWAKEICHI

Assoc. Prof.

Jiancang ZHUANG

Assoc. Prof.

Kenichiro SHIMATANI

Assoc. Prof.

Ikuko FUNATOGAWA

Assoc. Prof.

Shogo KATO

Assoc. Prof.

Yumi TAKIZAWA

Assoc. Prof.

Hisashi NOMA

Assist. Prof.

Takaaki SHIMURA

Assist. Prof.

Teppei OGIHARA

Visiting Prof.

Rinya TAKAHASHI

Project Assist. Prof. Yoshitake TAKEBAYASHI

26

Vice Director Manabu KUROKI

Project Assist. Prof. Takahiro OTANIVisiting Prof.

Yo SHIINA

Visiting Prof.

Manabu IWASAKI

Visiting Prof.

Toshiya SATO

Visiting Prof.

Satoshi TERAMUKAI

Visiting Prof.

Tatsuhiko TSUNODA

Visiting Prof.

Shigeyuki MATSUIVisiting Prof.

Naoki SAKAI

Visiting Prof.

Mihoko MINAMI

Visiting Prof.

Satoshi TAKIZAWA

Visiting Prof.

Toshihiro HORIGUCHI

Visiting Prof.

Naoto KUNITOMO

Visiting Prof.

Toshio HONDA

Visiting Prof.

Hiroshi TSUDA

Visiting Prof.

Sadaaki MIYAMOTO

Visiting Prof.

Michiko MIYAMOTO

Visiting Prof.

Toshinao YOSHIBA

Visiting Prof.

Tadashi ONO

Visiting Prof.

Nakahiro YOSHIDA

Visiting Prof.

Hideatsu TSUKAHARA

Visiting Prof.

Yasuhiro KUBOTA

Visiting Prof.

Yoichi NISHIYAMA

Visiting Prof.

Satoshi FUJII

Visiting Prof.

Takaaki YOSHINO

Visiting Prof.

Masaaki FUKASAWA

Organization

Staff
Risk Analysis Research Center
Visiting Prof.

Shusaku TSUMOTO

Visiting Prof.

Masakazu ANDO

Visiting Assoc. Prof. Hisateru TACHIMORI

Visiting Assoc. Prof. Makoto TOMITA

Visiting Assoc. Prof. Takafumi KUBOTA

Visiting Assoc. Prof. Toshikazu KITANO

Visiting Assoc. Prof. Hisayuki HARA

Visiting Assoc. Prof. Takashi KAMEYA

Visiting Assoc. Prof. Kenichi KAMO

Visiting Assoc. Prof. Masashi KONOSHIMA

Visiting Assoc. Prof. Takaki IWATA

Visiting Assoc. Prof. Bogdan Dumitru ENESCU

Visiting Assoc. Prof. Seisho SATO

Visiting Assoc. Prof. Hitoshi MOTOYAMA

Visiting Assoc. Prof. Kazushi MARUO

Visiting Assoc. Prof. Masakazu FURUKAWA

Visiting Assoc. Prof. Ryota NAKAMURA

Visiting Assoc. Prof. Yasutaka SHIMIZU

Visiting Assoc. Prof. Yukihiko OKADA

Visiting Assoc. Prof. Toshihiko KAWAMURA

Visiting Assoc. Prof. Haruhisa FUKUDA

Project Researcher

Syonosuke SUGASAWA

Project Researcher

Peng HAN

Research and Development Center for Data Assimilation
Director Tomoyuki HIGUCHI

Vice Director Yoshiyasu TAMURA

Prof.

Tomoyuki HIGUCHIProf.

Yoshiyasu TAMURA

Prof.

Junji NAKANOProf.

Yukito IBA

Assoc. Prof.

Genta UENO

Assoc. Prof.

Ryo YOSHIDA

Assoc. Prof.

Shinya NAKANO

Project Assoc. Prof. Masaya SAITO

Project Assist. Prof. Wu Stephen

Project Assist. Prof. Guillaume Lambard

Visiting Prof.

Takashi WASHIO

Visiting Prof.

Shinichi OTANIYoichi MOTOMURA

Visiting Prof.

Nobuhiko TERUIVisiting Prof.

Tadahiko SATO

Visiting Prof.

Visiting Assoc. Prof. Kazuyuki NAKAMURA

Visiting Assoc. Prof. Hiromichi NAGAO

Visiting Assoc. Prof. Hiroshi KATO

Visiting Assoc. Prof. Osamu HIROSE

Visiting Assoc. Prof. Hiroshi YAMASHITA

Visiting Assoc. Prof. Hiroshi FUJISAKI

Visiting Assoc. Prof. Tsukasa ISHIGAKI

Visiting Assoc. Prof. Eiji MOTOHASHI

Project Researcher

Project Researcher

Yuya ARIYOSHI

Kazue SUZUKI

Survey Science Center
Director Ryozo YOSHINO

Vice Director Tadahiko MAEDA

Ryozo YOSHINOProf

Takashi NAKAMURA

Prof

Takahiro TSUCHIYA

Assoc. Prof.

Tadahiko MAEDA

Assist. Prof.

Yoo Sung PARK

Visiting Prof.

Toru KIKKAWA

Visiting Prof.

Yoshimichi SATO

Visiting Prof.

Masato YONEDA

Visiting Prof.

Shintaro SONO

Visiting Prof.

Kazufumi MANABE

Visiting Prof.

Fumi HAYASHI

Visiting Prof.

Masahiro MIZUTA

Prof.

Visiting Assoc. Prof. Takahito ABE

Visiting Assoc. Prof. Wataru MATSUMOTO

Visiting Assoc. Prof. Koken OZAKI

Visiting Assoc. Prof. Tadayoshi FUSHIKI

Visiting Assoc. Prof. Hiroko TSUNODA

Visiting Assoc. Prof. Taisuke FUJITA

Visiting Assoc. Prof. Saeko KIKUZAWA

Research Center for Statistical Machine Learning
Director Kenji FUKUMIZU

Vice Director Tomoko MATSUIProf.

Kenji FUKUMIZU

Prof.

Tomoko MATSUIProf.

Shinto EGUCHIProf.

Yoshihiko MIYASATO

Prof.

Satoshi ITOProfShiro IKEDA

Prof

Satoshi KURIKI

Prof

Akiko TAKEDA

Assoc. Prof.

Daichi MOCHIHASHI

Assoc. Prof.

Shinsuke KOYAMA

Assoc. Prof.

Kazuhiro MINAMI

Project Assist. Prof. Song LIU

Project Assist. Prof. Takashi ARAI

Project Assist. Prof. Mikio MORIIVisiting Prof.

Masataka GOTO

Visiting Prof.

Visiting. Prof.

Hiroshi KURATA

Visiting Assoc. Prof. Yuji SHINANO

Katsuki FUJISAWA

Project Researcher

Motonobu KANAGAWA

Visiting Prof.

Takashi TSUCHIYA

Visiting Prof.

Yoshiki YAMAGATA

Visiting Assoc. Prof. Arthur GRETTON

27

Service Science Research Center
Director Junji NAKANOProf.

Tomoyuki HIGUCHIProf.

Tomoko MATSUIProf.

Junji NAKANOProf.

Manabu KUROKI

Assoc. Prof.

Kazuhiro MINAMI

Assist. Prof.

Nobuo SHIMIZU

Visiting Prof.

Yoichi MOTOMURA

Visiting Prof.

Shusaku TSUMOTO

Visiting Prof.

Nobuhiko TERUIVisiting Prof.

Tadahiko SATO

Visiting Prof.

Hiroshi MARUYAMA

Visiting Assoc. Prof. Tsukasa ISHIGAKI

Visiting Assoc. Prof. Yukihiko OKADA

Visiting Assoc. Prof. Eiji MOTOHASHI

Visiting Assoc. Prof. Toshihiko KAWAMURA

Visiting Assoc. Prof. Haruhisa FUKUDA

School of Statistical Thinking
Director Yoshinori KAWASAKI

Vice Director Jun ADACHIProf.

Satoshi ITO

Prof.

Yukito IBA

Prof.

Nobuhisa KASHIWAGI

Assoc. Prof.

Naomasa MARUYAMA

Adjunct Professor

Makio ISHIGURO

Adjunct Professor

Kunio SHIMIZU

Project Assist. Prof. Kaname MATSUE

Project Assist. Prof. Kotaro KAGAWA

Project Assist. Prof. Syogo MIZUTAKA

Project Assist. Prof. Naoki KAMIYA

Visiting Assoc. Prof. Osamu KOMORI

Visiting Assoc. Prof. Kei TAKAHASHI

Project Researchers
Project Assist. Prof. Hayafumi WATANABE

Project Assist. Prof. Takao KUMAZAWA

Project Assist. Prof. Yusuke INAGAKI

Project Assist. Prof. Kiyohisa SHIBAI

Project Assist. Prof. Shizu ITAKA

Center for Engineering and Technical Support
Director

Yoshinori KAWASAKI

Deputy Manager

Yuriko WATANABE

Head of Computing Facilities Unit

Kazuhiro NAKAMURA

Head of Media Development Unit

Akiko NAGASHIMA

Vice Director
Senior Specialist

Jun ADACHI
Saeko TANAKA

Head of Information Resources Unit

Saeko TANAKA

Library
Head Yoshinori KAWASAKI

Administration Planning and Coordination Section
Director Tomoyuki HIGUCHIHead of Planning Unit

Satoshi ITOHead of Evaluation Unit

Yoshiyasu TAMURA

Head of Information and Public Relations Unit

Koji KANEFUJI

Head of Intellectual Property Unit

Satoshi ITOHead of NOE Promotion Unit

Satoshi ITO• URA Station ————————————————————————————————————————————————————————————————————————

28

Leader

Kozo KITAMURA

SubleaderMotoi OKAMOTO

Research Administrator

Keisuke HONDA

Research Administrator

Yoko OGAWA

Organization

Staff
Administration Planning and Coordination Section• Office of Female Researcher Development, ISM —————————————————————————————————————————————————
Head of Office of Female Researcher Development,ISM Koji KANEFUJI

NIPR/ISM Joint Administration OfficeDirector of NIPR/ISM Joint Administration OfficeKazuhiko HASEGAWA

Director of General Service Center

Katsunori NOZUMI• Planning Section ( ISM ) —————————————————————————————————————————————————————————————————————
Head of Planning Section Toyoharu HAYASHIDA
Deputy Head

Masatoshi NAKAMURATeam Leader

Junko MATSUYAMATeam Leader

Koji MORITATeam Leader

Ichiro KAWAJI• Planning Section ( NIPR) ————————————————————————————————————————————————————————————————————
Head of Planning Section Yuki OYAMA
Deputy Head

Motokazu TOYODATeam Leader

Hiromi OBAMATeam Leader

Hitoshi HIRAYAMATeam Leader

Michihito SAKURAITeam Leader

Masaki ONIZAWA• General Service Center —————————————————————————————————————————————————————————————————————
Vice Director of General Service Center Tatsuya NAKANO
Deputy Head

Koji SAKAMOTO

Deputy Head

Yutaka ONO

Deputy HeadTomohiko MIYAUCHITeam Leader

Yoshihiro YAMADATeam Leader

Hiroaki ARAI

Team Leader

Kentaro TSUJIITeam Leader

Hiromi KATOTeam Leader

Takuya SAITO

Specialist

Yoriko HAYAKAWA

Specialist

Takeshi KUWAHARA

Specialist

Junichi NAKATA

Council of The Institute of Statistical Mathematics ( As of April 1, 2016)
Yasushi AKIYAMA

Professor, Graduate School of Information Science and Engineering, Tokyo Institute of Technology

Masahiro MIZUTA

Professor, Information Initiative Center, and Graduate School of Information Science and Technology, Hokkaido University

Shigeru OBAYASHI

Director Institute of Fluid Science Tohoku University

Nakahiro YOSHIDA

Professor, Graduate School of Mathematical Sciences, The University of Tokyo

Masayuki UCHIDA

Professor, Graduate School of Engineering Science, Osaka University

Ryuei NISHII

Professor, Institute of Mathematics for Industry, Kyushu University

Yasuhiro OMORI

Professor, Faculty of Economics, University of Tokyo

Shoichi YOKOYAMA

Professor, Language Change Division, National Institute for Japanese Language and Linguistics

Masato OKADA

Professor, Graduate School of Frontier Sciences, The University of Tokyo

Michiko WATANABE

Professor, The Graduate School of Health Management, Keio University

Tomoko MATSUI

Professor, Research Organization of Information and Systems

Ryozo YOSHINO

Professor, Research Organization of Information and Systems

Yoshiyasu TAMURA

Professor (Vice Director-General, ISM )Satoshi ITOProfessor ( Vice Director-General, ISM )Kouji KANEFUJI

Professor ( Vice Director-General, ISM )Junji NAKANOProfessor (Director of Department of Statistical Modeling, ISM )Satoshi YAMASITA

Professor (Director of Department of Data Science, ISM )29

Organization

Council of The Institute of Statistical Mathematics
Satoshi KURIKI

Professor (Director of Department of Mathematical Analysis and Statistical Inference, ISM )Yoshinori KAWASAKI

Professor (Director of Center for Engineering and Technical Support, ISM )Yoshihiko MIYASATO

Professor (Department of Mathematical Analysis and Statistical Inference, ISM )Kenji FUKUMIZU

Professor (Department of Mathematical Analysis and Statistical Inference, ISM )Cooperative Research Committee ( As of April 1, 2016)
Tadahiko SATO

Professor, Faculty of Business Sciences, University of Tsukuba

Akinobu TAKEUCHI

Associate Professor, Faculty of Humanities and Social Sciences, Jissen Women’s University

Shigeyuki MATSUI

Professor, Graduate School of Medicine, Nagoya University

Hiroshi YADOHISA

Professor, Faculty of Culture and Information Science, Doshisha University

Kazue YAMAOKA

Professor, Graduate School of Public Health, Teikyo University

Junji NAKANOProfessor (Director of Department of Statistical Modeling, ISM )Satoshi YAMASHITA

Professor (Director of Department of Data Science, ISM )Satoshi ITOProfessor ( Vice Director-General, ISM )Shinto EGUCHI

Professor (Department of Mathematical Analysis and Statistical Inference, ISM )Research Ethics Review Committee ( As of April 1, 2016)
Specialist on epidemiology and social research

Kazuo SEIYAMA

Professor Emeritus, Tokyo University
Deputy Director, Research Center for Science Systems of Japan Society for
the Promotion of Science

Specialist on epidemiology and social research

Keiko SATO

Associate professor, Kyoto University Hospital, Institute for Advancement ofClinical and Translational Science Department of EBM ResearchSpecialist in the field of ethics and law

Hitomi NAKAYAMA

Lawyer, Kasumigaseki-Sogo Law Offices

Person in citizen’s position

Yutaka KURIKI

Kindergarten Director and Elementary School Director, KEIMEI GAKUEN

Research education staff of ISM

Takashi NAKAMURA

Professor ( Department of Data Science, ISM)

Research education staff of ISM

Koji KANEFUJI

Professor ( Vice Director-General, ISM)

Research education staff of ISM

Tadahiko MAEDA

Associate Professor ( Department of Data Science, ISM)

Research education staff of ISM

Ikuko FUNATOGAWA

Associate Professor ( Department of Data Science, ISM)

Research education staff of ISM

Shuhei MANO

Associate Professor (Department of Mathematical Analysis and Statistical
Inference, ISM )Professor Emeritus ( As of April 1, 2016 )30

Kameo MATUSITA

Sigeki NISIHIRA

Tatsuzo SUZUKI

Giitiro SUZUKI

Ryoichi SHIMIZU

Noboru OHSUMI

Masakatsu MURAKAMI

Kunio TANABE

Tadashi MATSUNAWA

Masami HASEGAWA

Yoshiyuki SAKAMOTO

Takemi YANAGIMOTO

Yoshiaki ITOH

Yasumasa BABA

Katsuomi HIRANO

Masaharu TANEMURA

Makio ISHIGURO

Yosihiko OGATA

Hiroe TSUBAKI

History

1944

June

Based on a proposal submitted at an academic study conference in December 1943, the organization was
founded as an institute under the direct control of the Ministry of Education. This proposal aimed to provide
supervision for studies looking into the mathematical principles of probability and their application, and was
also intended to facilitate, unify and promote the publication of research results.

1947

April

The affiliated statistical specialists’ school was opened.

May

The Institute was divided into the 1st Research Dept. ( fundamental theories ), the 2nd Research Dept. (statisticaltheories for the natural sciences), and the 3rd Research Dept. ( statistical theories for the social sciences ).

1949

June

The Institute was placed under the control of the Ministry of Education because of the enforcement of the Ministryof Education Establishment Law.

1955

September

Reorganized into the 1st Research Dept. ( fundamental theories ), the 2nd Research Dept. ( natural and social science
theories ), and the 3rd Research Dept. ( operations, research, statistical analysis theories ). The laboratory
system, comprising 9 laboratories and the research guidance promotion room, was adopted.

1969

October

1971

April

A new office building was constructed in Minato Ward.
The 4th Research Dept. (informatics theories) was instituted.

1973

April

The 5th Research Dept. (prediction and control theories) was instituted.
The 6th Research Dept. (statistical theories of human behavior) was instituted.

1975

October

1979

November

1985

April

Repositioned as a National Inter-University Research Institute due to the regulation change. The new mission
includes providing facilities and skills to other universities, in addition to conducting cutting-edge research on
statistical mathematics. Accordingly, the institute was reorganized into four basic research departments (Fundamental Statistical Theory, Statistical Methodology, Prediction & Control, and Interdisciplinary Statistics ) andtwo strategic centers ( Statistical Data Analysis Center and Statistical Education & Information Center ). The
Statistical Technical Training Center was terminated.

1988

October

The Dept. of Statistical Science was instituted in the School of Mathematical and Physical Science, part of the
Graduate University for Advanced Studies ( SOKENDAI ).

1989

June

The Institute was reorganized as an Inter-University Research Institute based on the National School Establishment Law.

1993

April

The Planning Coordination Chief System was instituted.

1997

April

The affiliated Statistical Data Analysis Center was reorganized into the Center for Development of StatisticalComputing, and the Statistical Education and Information Center was reorganized into the Center for Information
on Statistical Sciences.

2003

September

2004

April

The Institute was reorganized into the Institute of Statistical Mathematics, part of the Research Organi­zation of Information and Systems of the Inter-University Research Institute based on the National University Corporation Law.
The Planning Coordination Chief System was abolished and the position of Vice Director-General was instituted instead. The Dept. of Statistical Science in the School of Mathe­matical and Physical Science, SOKENDAI, was reorganized. In addition, the Dept. of Statistical Science and the School of Multidisciplinary Sciences were instituted.

2005

April

The research organization was reorganized into three research departments ( the Department of Statis­tical
Modeling, the Department of Data Science, and the Department of Mathematical Analysis and Statistical Inference ). The affiliated Center for Development of Statistical Computing, the Center for Information on Statistical
Sciences, and the Engineering and Technical Services Section were inte­grated into the Center for Engineeringand Technical Support. The Risk Analysis Research Center was instituted.

2006

April

The Administration Planning Coordination Unit was instituted.

2008

April

The Research Innovation Center was instituted.
The Administration Planning and Coordination Unit was reorganized into the Administration Planning and Coordination Section (hereafter APCS), within which the Intellectual Property Unit, the Evaluation Unit and the Information and Public Relations Unit were instituted.

2009

January

The Planning Unit was instituted within APCS.

October

The Institute was moved to 10-3 Midori-cho, Tachikawa, Tokyo.

2010

The Information Research Building was constructed.

The Prediction and Knowledge Discovery Research Center was instituted.

June

Officially opened the Akaike Guest House.

July

Reorganized the Administration Office to create the NIPR/ISM Joint Administration Office and launch the General
Service Center. The NOE Forwarding Unit ( now we call “NOE Promotion Unit” ) was instituted within APCS.

2011

January

Research and Development Center for Data Assimilation was instituted.
Survey Science Center was instituted.

2012

January

Research Center for Statistical Machine Learning, Service Science Research Center and School of StatisticalThinking were instituted.

2014

July
December

The URA Station was instituted within the Planning Unit.
The Office of Female Researcher Development was instituted within the Planning Unit.

31

The Institute of
Statistical Mathematics(

Bus stop
TachikawaShiyakusho

to Kamikitadai

Tachikawa City Hall
Bus stop ( Tachikawa Academic Plaza))

Bus stop (Saibansho-mae)
Courthouse

Takamatsu

Local Autonomy
College

National Institutefor Japanese Language
and Linguistics

Tachikawa
Police Station
Disaster
MedicalCenterTama
MonorailShowa Kinen Park

Ome Line

Tachikawa-kita

Tachikawa
Access to the ISM

Chuo Lineto Shinjuku,
Tokyo →

Tama Monorail—10 min walk from Takamatsu Sta.
Tachikawa Bus— Tachikawa Academic Plaza bus stop
— 5 min walk from Saibansho-mae
or Tachikawa-Shiyakusho bus stop

10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan
Tel: +81- ( 0 ) 50-5533-8500 Fax: +81- ( 0 ) 42-527-9302

http://www.ism.ac.jp/